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Titlebook: Bridging the Semantic Gap in Image and Video Analysis; Halina Kwaśnicka,Lakhmi C. Jain Book 2018 Springer International Publishing AG 2018

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期刊全称Bridging the Semantic Gap in Image and Video Analysis
影响因子2023Halina Kwaśnicka,Lakhmi C. Jain
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发行地址Presents cutting-edge research on various ways to bridge the semantic gap in image and video analysis.Explores the correspondence between the low-level information extracted from an image and the mean
学科分类Intelligent Systems Reference Library
图书封面Titlebook: Bridging the Semantic Gap in Image and Video Analysis;  Halina Kwaśnicka,Lakhmi C. Jain Book 2018 Springer International Publishing AG 2018
影响因子.This book presents cutting-edge research on various ways to bridge the semantic gap in image and video analysis. The respective chapters address different stages of image processing, revealing that the first step is a future extraction, the second is a segmentation process, the third is object recognition, and the fourth and last involve the semantic interpretation of the image..The semantic gap is a challenging area of research, and describes the difference between low-level features extracted from the image and the high-level semantic meanings that people can derive from the image. The result greatly depends on lower level vision techniques, such as feature selection, segmentation, object recognition, and so on. The use of deep models has freed humans from manually selecting and extracting the set of features. Deep learning does this automatically, developing more abstract features at the successive levels.. .The book offers a valuable resource for researchers, practitioners, students and professors in Computer Engineering, Computer Science and related fields whose work involves images, video analysis, image interpretation and so on..
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Bridging the Semantic Gap in Image and Video Analysis978-3-319-73891-8Series ISSN 1868-4394 Series E-ISSN 1868-4408
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Siavash H. Khajavi,Mika Salmi,Jan Holmströmage, a set of discriminative patterns (hereinafter keypoints) present on some parts of background and/or foreground elements of the image itself. A prerequisite of a wide range of practical applications (e.g., vehicle tracking, person re-identification) is the design and development of algorithms ab
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https://doi.org/10.1007/978-0-306-48567-1accurately as possible, areas with a certain level of semantic uniformity on top of the default pictorial uniformity of regions extracted by the segmentation methods. Obviously, the results should be insensitive to noise, textures, and other effects typically distorting such uniformities. This chapt
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